随着我们周围智能设备数量的增加，创建具有社会感知能力的智能系统的机会也在增多。在此背景下，移动设备可用于收集学生数据，从而更好地理解他们的日常作息如何影响学业表现。此外，Covid-19疫情也给学生带来了新的挑战和困难，并对其生活方式产生了相当大的影响。本文提出了一个通过智能手机应用程序 ISABELA 收集的数据集，其中包含被动数据（例如活动和位置）以及来自问卷的自我报告数据。我们使用不同的机器学习模型进行了多项测试，以对学生表现进行分类。这些测试在不同时间窗口下开展，结果表明每周时间窗口比每月时间窗口能带来更好的预测和分类效果。此外，结果表明，即使使用在不同情境（即 Covid-19疫情之前和疫情期间）收集的数据，所建立的模型也能够预测学生表现。SVM、XGBoost 以及结合 Random Forest 的 AdaBoost-SAMME 被发现是最佳算法，准确率超过78%。另外，我们提出了一种使用决策级中位数投票算法的流水线，通过利用学生的历史数据进一步改进预测，从而提升模型性能。借助该流水线，可以进一步提高模型性能，其中部分模型取得了超过90%的准确率。
英文摘要：
As the number of smart devices that surround us increases, so do the opportunities to create smart socially-aware systems. In this context, mobile devices can be used to collect data about students and to better understand how their day-to-day routines can influence their academic performance. Moreover, the Covid-19 pandemic led to new challenges and difficulties, also for students, with considerable impact on their lifestyle. In this paper we present a dataset collected using a smartphone application (ISABELA), which include passive data (e.g., activity and location) as well as self-reported data from questionnaires. We present several tests with different machine learning models, in order to classify students' performance. These tests were carried out using different time windows, showing that weekly time windows lead to better prediction and classification results than monthly time windows. Furthermore, it is shown that the created models can predict student performance even with data collected from different contexts, namely before and during the Covid-19 pandemic. SVMs, XGBoost and AdaBoost-SAMME with Random Forest were found to be the best algorithms, showing an accuracy greater than 78%. Additionally, we propose a pipeline that uses a decision level median voting algorithm to further improve the models' performance, by using historic data from the students to further improve the prediction. Using this pipeline, it is possible to further increase the performance of the models, with some of them obtaining an accuracy greater than 90%.
There is a burgeoning trend of smartphone ownership in Africa due to the low costs of Android smartphones and the global increase in social media usage. Building upon previous works that introduced a smartphone-based coding course to secondary and tertiary students in Ghana via an in-person program and an online course, this work introduced Africans in 37 countries to our online smartphone-based course in 2019. Students in this 8-week course read lesson notes, submitted assignments, collaborated with peers, and facilitators in an online forum and completed open and closed-ended surveys after t…
With the outbreak of the COVID-19 pandemic in 2020, most colleges and universities move to restrict campus activities, reduce indoor gatherings and move instruction online. These changes required that students adapt and alter their daily routines accordingly. To investigate patterns associated with these behavioral changes, we collected smartphone sensing data using the Beiwe platform from two groups of undergraduate students at a major North American university, one from January to March of 2020 (74 participants), the other from May to August (52 participants), to observe the differences in s…
Unlocking Mental Health: Exploring College Students' Well-being through Smartphone Behaviors
Department of Economics University of Southern California（经济学系 美国南加州大学）；Electrical and Computer Engineering Purdue University（电气与计算机工程系 帕克森大学）；Information Sciences Institute University of Southern California（信息科学研究所 美国南加州大学）
"Why SuaCode?": Understanding African Students' Motivations for Taking a Smartphone-Based Online Coding Course
University of Ghana（加纳大学）；Kwame Nkrumah University of Science and Technology（科怀·恩克鲁玛科技大学）；Kwame AI Inc.（Kwame AI 公司）；Charite Berlin（柏林夏里特医院）；Georgia Institute of Technology（佐治亚理工学院）；ETH Zurich（苏黎世联邦理工学院）
The technological advances in the smartphone application market has expanded at great lengths in recent years (Kaya, Ozturk & Gumussoy 2019). One of the main smartphone applications that often sees record breaking downloads are gaming apps. The gaming application that recently received the most downloads of all time, 100 million in one week, was the mobile game Call of Duty (CoD). With the game generating revenues of over $8 million across app stores, it is important that the users are satisfied with the game and its experience (Cuthbertson, 2019). Unlike other applications, gamers are drawn t…
Loneliness is a widely affecting mental health symptom and can be mediated by and co-vary with patterns of social exposure. Using momentary survey and smartphone sensing data collected from 129 Android-using college student participants over three weeks, we (1) investigate and uncover the relations between momentary loneliness experience and companionship type and (2) propose and validate novel geosocial features of smartphone-based Bluetooth and GPS data for predicting loneliness and companionship type in real time. We base our features on intuitions characterizing the quantity and spatiotemp…
Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning
The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Carnegie Mellon University（卡内基梅隆大学）；The University of Pittsburgh（匹兹堡大学）；The University of Hong Kong（香港大学）
Human Activity Recognition (HAR) enables context-aware user experiences where mobile apps can alter content and interactions depending on user activities. Hence, smartphones have become valuable for HAR as they allow large, and diversified data collection. Although previous work in HAR managed to detect simple activities (i.e., sitting, walking, running) with good accuracy using inertial sensors (i.e., accelerometer), the recognition of complex daily activities remains an open problem, specially in remote work/study settings when people are more sedentary. Moreover, understanding the everyday …
Circadian rhythm is the natural biological cycle manifested in human daily routines. A regular and stable rhythm is found to be correlated with good physical and mental health. With the wide adoption of mobile and wearable technology, many types of sensor data, such as GPS and actigraphy, provide evidence for researchers to objectively quantify the circadian rhythm of a user and further use these quantified metrics of circadian rhythm to infer the user's health status. Researchers in computer science and psychology have investigated circadian rhythm using various mobile and wearable sensors in…
An Offline Mobile Conversational Agent for Mental Health Support: Learning from Emotional Dialogues and Psychological Texts with Student-Centered Evaluation
Department of Electrical Engineering and Computer Science, Indian Institute of Science Education and Research Bhopal（电子工程与计算机科学系，比哈尔印度科学教育与研究中心）
While the task of automatically detecting eating events has been examined in prior work using various wearable devices, the use of smartphones as standalone devices to infer eating events remains an open issue. This paper proposes a framework that infers eating vs. non-eating events from passive smartphone sensing and evaluates it on a dataset of 58 college students. First, we show that time of the day and features from modalities such as screen usage, accelerometer, app usage, and location are indicative of eating and non-eating events. Then, we show that eating events can be inferred with an…
One fundamental goal of learning is preparation for future learning (PFL) and being able to extend acquired skills and problem-solving strategies to different domains and environments. While substantial research has shown that PFL can be accelerated by obtaining metacognitive skills or influenced by the individual's motivation, no prior work investigated whether the interaction of the two factors could assess students' competency for PFL. In this chapter, we tackle this research question in one type of highly interactive e-learning environment, intelligent tutoring systems. More specifically, …
